A Fuzzy ART2 Model for Finding Association Rules in Medical Data

Huang, Yo-Ping; Vu, Thi Thanh Hoa; Jau, Jung-Shian; Sandnes, Frode Eika
Chapter, Peer reviewed
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URI
https://hdl.handle.net/10642/499
Date
2010-09
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Original version
Huang, Y., Vu, T., Jau, J. & Sandnes, F.E. (2010). A Fuzzy ART2 Model for Finding Association Rules in Medical Data. In: P. Sobrevilla (Ed.), IEEE World Congress on Computational Intelligence. Piscataway, N.J. : Institute of Electrical and Electronics Engineers   http://dx.doi.org/10.1109/FUZZY.2010.5584780
Abstract
This paper describes a model that discovers

association rules from a medical database to help doctors treat

and diagnose a group of patients who show similar prehistoric

medical symptoms. The proposed data mining procedure

consists of two modules. The first is a clustering module that is

based on a neural network, Adaptive Resonance Theory 2

(ART2), which performs affinity grouping tasks on a large

amount of medical records. The other module employs fuzzy set

theory to extract fuzzy association rules for each homogeneous

cluster of data records. In addition, an example is given to

illustrate this model. Simulation results show that the proposed

algorithm can be used to obtain the desired results with a

reduced processing time.
Publisher
IEEE Computer Society